




'''simple docstring'''



import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

     from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
     UpperCAmelCase_							:	List[Any]	     =		False

if is_vision_available():
     from PIL import Image

     from transformers import PixaStructImageProcessor





class      lowerCAmelCase (			unittest.TestCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=7     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=18     ,	__SCREAMING_SNAKE_CASE=30     ,	__SCREAMING_SNAKE_CASE=400     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=None     ,	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	size if size is not None else {"""height""": 20, """width""": 20}
            __snake_case            =	parent
            __snake_case            =	batch_size
            __snake_case            =	num_channels
            __snake_case            =	image_size
            __snake_case            =	min_resolution
            __snake_case            =	max_resolution
            __snake_case            =	size
            __snake_case            =	do_normalize
            __snake_case            =	do_convert_rgb
            __snake_case            =	[512, 1024, 2048, 4096]
            __snake_case            =	patch_size if patch_size is not None else {"""height""": 16, """width""": 16}


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	"""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
            __snake_case            =	Image.open(requests.get(SCREAMING_SNAKE_CASE_     ,	stream=SCREAMING_SNAKE_CASE_	).raw	).convert('''RGB'''	)
            return raw_image





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11					, reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''					, )
@require_torch
@require_vision
class      lowerCAmelCase (			_lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   Tuple										=      PixaStructImageProcessor if is_vision_available() else None


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	PixaStructImageProcessingTester(self	)


       @property
       def    lowerCAmelCase   ( self	)   ->	List[str]:



            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()


       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'''do_normalize'''	)	)
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'''do_convert_rgb'''	)	)


       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case            =	self.image_processor_tester.prepare_dummy_image()

            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            __snake_case            =	2048

            __snake_case            =	image_processor(SCREAMING_SNAKE_CASE_     ,	return_tensors='''pt'''     ,	max_patches=SCREAMING_SNAKE_CASE_	)
            self.assertTrue(torch.allclose(inputs.flattened_patches.mean()     ,	torch.tensor(0.0_606	)     ,	atol=1E-3     ,	rtol=1E-3	)	)


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            # create random PIL images
            __snake_case            =	prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=SCREAMING_SNAKE_CASE_	)
            for image in image_inputs:
                 self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	Image.Image	)

            # Test not batched input
            __snake_case            =	(
                (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 __snake_case            =	image_processor(
                     image_inputs[0]     ,	return_tensors='''pt'''     ,	max_patches=SCREAMING_SNAKE_CASE_	).flattened_patches
                 self.assertEqual(
                     encoded_images.shape     ,	(1, max_patch, expected_hidden_dim)     ,	)

                 # Test batched
                 __snake_case            =	image_processor(
                     SCREAMING_SNAKE_CASE_     ,	return_tensors='''pt'''     ,	max_patches=SCREAMING_SNAKE_CASE_	).flattened_patches
                 self.assertEqual(
                     encoded_images.shape     ,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,	)


       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            # create random PIL images
            __snake_case            =	prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=SCREAMING_SNAKE_CASE_	)
            for image in image_inputs:
                 self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	Image.Image	)

            # Test not batched input
            __snake_case            =	(
                (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
                * self.image_processor_tester.num_channels
            ) + 2

            __snake_case            =	True

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 with self.assertRaises(SCREAMING_SNAKE_CASE_	):
                      __snake_case            =	image_processor(
                          image_inputs[0]     ,	return_tensors='''pt'''     ,	max_patches=SCREAMING_SNAKE_CASE_	).flattened_patches

                 __snake_case            =	"""Hello"""

                 __snake_case            =	image_processor(
                     image_inputs[0]     ,	return_tensors='''pt'''     ,	max_patches=SCREAMING_SNAKE_CASE_     ,	header_text=SCREAMING_SNAKE_CASE_	).flattened_patches
                 self.assertEqual(
                     encoded_images.shape     ,	(1, max_patch, expected_hidden_dim)     ,	)

                 # Test batched
                 __snake_case            =	image_processor(
                     SCREAMING_SNAKE_CASE_     ,	return_tensors='''pt'''     ,	max_patches=SCREAMING_SNAKE_CASE_     ,	header_text=SCREAMING_SNAKE_CASE_	).flattened_patches
                 self.assertEqual(
                     encoded_images.shape     ,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,	)


       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            # create random numpy tensors
            __snake_case            =	prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=SCREAMING_SNAKE_CASE_     ,	numpify=SCREAMING_SNAKE_CASE_	)
            for image in image_inputs:
                 self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	np.ndarray	)

            __snake_case            =	(
                (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 __snake_case            =	image_processor(
                     image_inputs[0]     ,	return_tensors='''pt'''     ,	max_patches=SCREAMING_SNAKE_CASE_	).flattened_patches
                 self.assertEqual(
                     encoded_images.shape     ,	(1, max_patch, expected_hidden_dim)     ,	)

                 # Test batched
                 __snake_case            =	image_processor(
                     SCREAMING_SNAKE_CASE_     ,	return_tensors='''pt'''     ,	max_patches=SCREAMING_SNAKE_CASE_	).flattened_patches
                 self.assertEqual(
                     encoded_images.shape     ,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,	)




       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            # create random PyTorch tensors
            __snake_case            =	prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=SCREAMING_SNAKE_CASE_     ,	torchify=SCREAMING_SNAKE_CASE_	)
            for image in image_inputs:
                 self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	torch.Tensor	)

            # Test not batched input
            __snake_case            =	(
                (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 __snake_case            =	image_processor(
                     image_inputs[0]     ,	return_tensors='''pt'''     ,	max_patches=SCREAMING_SNAKE_CASE_	).flattened_patches
                 self.assertEqual(
                     encoded_images.shape     ,	(1, max_patch, expected_hidden_dim)     ,	)

                 # Test batched
                 __snake_case            =	image_processor(
                     SCREAMING_SNAKE_CASE_     ,	return_tensors='''pt'''     ,	max_patches=SCREAMING_SNAKE_CASE_	).flattened_patches
                 self.assertEqual(
                     encoded_images.shape     ,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,	)





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11					, reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''					, )
@require_torch
@require_vision
class      lowerCAmelCase (			_lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   str										=      PixaStructImageProcessor if is_vision_available() else None


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	PixaStructImageProcessingTester(self     ,	num_channels=4	)
            __snake_case            =	3


       @property
       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'''do_normalize'''	)	)
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_     ,	'''do_convert_rgb'''	)	)




       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            # create random PIL images
            __snake_case            =	prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=SCREAMING_SNAKE_CASE_	)
            for image in image_inputs:
                 self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,	Image.Image	)

            # Test not batched input
            __snake_case            =	(
                (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
                * (self.image_processor_tester.num_channels - 1)
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 __snake_case            =	image_processor(
                     image_inputs[0]     ,	return_tensors='''pt'''     ,	max_patches=SCREAMING_SNAKE_CASE_	).flattened_patches
                 self.assertEqual(
                     encoded_images.shape     ,	(1, max_patch, expected_hidden_dim)     ,	)

                 # Test batched
                 __snake_case            =	image_processor(
                     SCREAMING_SNAKE_CASE_     ,	return_tensors='''pt'''     ,	max_patches=SCREAMING_SNAKE_CASE_	).flattened_patches
                 self.assertEqual(
                     encoded_images.shape     ,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     ,	)











def  lowerCAmelCase_	(lowerCAmelCase__:		Union[str, Any]		,						lowerCAmelCase__:		Optional[int]  ):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]							      =     0
			UpperCAmelCase_:		Tuple							      =     len(lowerCAmelCase__  ) - 1

			while left <= right:
						# avoid divided by 0 during interpolation
						if sorted_collection[left] == sorted_collection[right]:
									if sorted_collection[left] == item:
												return left
									else:
												return None

						UpperCAmelCase_:		Dict							      =     left + ((item - sorted_collection[left]) * (right - left)) // (
						    sorted_collection[right] - sorted_collection[left]
						)

						# out of range check
						if point < 0 or point >= len(lowerCAmelCase__  ):
									return None

						UpperCAmelCase_:		str							      =     sorted_collection[point]
						if current_item == item:
									return point
						else:
									if point < left:
												UpperCAmelCase_:		int							      =     left
												UpperCAmelCase_:		str							      =     point
									elif point > right:
												UpperCAmelCase_:		List[str]							      =     right
												UpperCAmelCase_:		Optional[int]							      =     point
									else:
												if item < current_item:
															UpperCAmelCase_:		Optional[Any]							      =     point - 1
												else:
															UpperCAmelCase_:		Any							      =     point + 1
			return None







def  lowerCAmelCase_	(lowerCAmelCase__:		Dict		,						lowerCAmelCase__:		List[Any]		,						lowerCAmelCase__:		Any		,						lowerCAmelCase__:		Any  ):





			"""simple docstring"""


			if sorted_collection[left] == sorted_collection[right]:
						if sorted_collection[left] == item:
									return left
						else:
									return None

			UpperCAmelCase_:		Union[str, Any]							      =     left + ((item - sorted_collection[left]) * (right - left)) // (
			    sorted_collection[right] - sorted_collection[left]
			)

			# out of range check
			if point < 0 or point >= len(lowerCAmelCase__  ):
						return None

			if sorted_collection[point] == item:
						return point
			elif point < left:
						return interpolation_search_by_recursion(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__  )
			elif point > right:
						return interpolation_search_by_recursion(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__  )
			else:
						if sorted_collection[point] > item:
									return interpolation_search_by_recursion(
									    lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						point - 1  )
						else:
									return interpolation_search_by_recursion(
									    lowerCAmelCase__		,						lowerCAmelCase__		,						point + 1		,						lowerCAmelCase__  )







def  lowerCAmelCase_	(lowerCAmelCase__:		Dict  ):





			"""simple docstring"""


			if collection != sorted(lowerCAmelCase__  ):
						raise ValueError("""Collection must be ascending sorted"""  )
			return True


if __name__ == "__main__":
	import sys
	a      :  Optional[Any]			     =			0
	if debug == 1:
		a      :  int			     =			[10, 30, 40, 45, 50, 66, 77, 93]
		try:
			__assert_sorted(collection)
		except ValueError:
			sys.exit('Sequence must be ascending sorted to apply interpolation search')
		a      :  int			     =			67

	a      :  Any			     =			interpolation_search(collection, target)
	if result is not None:
		print(F'''{target} found at positions: {result}''')
	else:
		print('Not found')






